A Group Recognition Method of Scientific and Technological Personnel based on Relational Graph

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI:10.1109/ICSS50103.2020.00017

Zhuohao Wang, Dongju Yang, Hanshuo Zhang

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Abstract

The key problem in the fine management of science and technology is to model the behavior characteristics of scientific and technical personnel and then find groups through various related cooperative relationships. Aiming at the analysis of team relationship of scientific and technical personnel data, this paper proposed a method to recognize the group of scientific and technological personnel based on relational graph. The relationship model of scientific and technological personnel was designed, and based on this, the relational graph was constructed with the relationship identification and extraction from source data. A frequent item mining algorithm based on Hadoop was proposed, which enabled getting the group of scientific and technological personnel by mining and analysis of data in relational graph. In this paper, the proposed method was experimented on both open and private data sets, and compared with several classical algorithms. The results showed that the method proposed in this paper has a significant improvement in execution efficiency.

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基于关系图的科技人员群体识别方法

科技精细化管理的关键问题是对科技人员的行为特征进行建模，然后通过各种相关的合作关系找到群体。针对科技人员数据的团队关系分析，提出了一种基于关系图的科技人员群体识别方法。设计了科技人员关系模型，在此基础上，通过对源数据的关系识别和提取，构建了科技人员关系图。提出了一种基于Hadoop的频繁项挖掘算法，通过对关系图中的数据进行挖掘和分析，得到科技人员群体。本文分别在公开数据集和私有数据集上进行了实验，并与几种经典算法进行了比较。结果表明，本文提出的方法在执行效率上有显著提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2020 International Conference on Service Science (ICSS)

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